Office Hours: Episode 5

Episode Extras

• Learn More About Dr. Ayokunle Olanrewaju

  Dr. Ayokunle Olanrewaju was born in Sagamu, Nigeria. He attended Mayflower School Ikenne where he developed a love for all things math and science. He moved to Fort St John, Canada in high school and attended North Peace Secondary School. Subsequently, he received Bachelor’s and Master’s degrees in Electrical Engineering from the University of Alberta in Edmonton, Canada. It was at the University of Alberta that he was first introduced to microfluidics & lab-on-a-chip devices with sci-fi inspired dreams of tricorder toolkits. He moved to Montréal to complete a PhD in Bioengineering focused on 3D-printing self-powered autonomous microfluidics for point-of-care diagnostic assays. To develop closer collaborations with clinicians and to connect with the people-driven essence of diagnostics development, he moved to the University of Washington (UW) for a postdoc focused on therapeutic monitoring of antiretroviral medications. He became an Acting Assistant Professor in the Mechanical Engineering Department in June 2020 and started the Ọlánrewájú lab – jointly in the Bioengineering and Mechanical Engineering departments – in January 2022.

• Read the Transcript

  [Music] 

  Liz Wayne: everyone, and welcome to the office hours with Liz Wayne, a brand new podcast brought to you by the Biomedical Engineering Society. I'm Liz, an assistant professor in bioengineering, and I'm going to introduce you to the world of biomedical engineering through my eyes or my voice. From genes to machines, biomedical engineers can do it all. We'll dive into how discoveries are made, how research becomes medicine, and what it's actually like working in academia today. So, whether you're a student, researcher, educator, or just someone who is curious about science and how the academic world works, you've come to the right place.  

  I have brought a guest to office hours who might know the answer to how to make tests for everything to make my life simple, and he's laughing at me right now because he's waiting for his intro. Dr Ayo Olanrewaju, he's an assistant professor at the University of Washington in biomedical engineering and mechanical engineering, and he's an expert in making tests using micro fluidics. Welcome to the show, Ayo! 

  Ayo Olanrewaju: Thank you, Thank you. Thank you for having me.  

  Liz: I'm really excited to see you in this format. I am so fortunate that I get to see you a lot actually these days, and now I asked you to record it. So does that feel more awkward?  

  Ayo: You know, it's just another day in the life. 

  Liz: And so you're making a career out of developing user-friendly tests. So how do you do that? What does that mean? 

  Ayo: User friendly tests, or make a career? 

  Liz: You could talk about the last part, but let's assume that we are doing really well today, and you are. 

  Ayo: Yes, good day.  

  Liz: How do you make a career out of making really complicated things simple?  

  Ayo: Yeah, no. So, great question. I will also start with a big disclaimer. So, my PhD was in microfluidics, and I got a PhD, I like to say, in the time of Theranos. And if there are listeners who have not heard of Theranos, this was a company that claimed they could measure basically everything under the sun using the power of microfluidics. And it turns out it was a scam.  

  Liz: Oh, I didn't know they were using microfluidics. I knew about the blood, but I didn't know... 

  Ayo: They started off with this idea of a microtainer, and they were using microfluidics at one point, or claimed to be, and then eventually pivoted away from it. But the idea of microfluidics, and broadly speaking, lab-on-a-chip devices, has been around for some time, very compelling, late 80s, early 90s. And one way I like to think about it is this idea of you want to use tools that match what you're looking for. If anybody ever watched the like, Inspector Gadget cartoon back in the day, this is dating myself, but it's this idea that, like, you had this inspector that would walk around, and if you're trying to get a kitten from a tree, then you, like, extend your arm and you grab that, Oh, you got my magnifying lens. And so, the idea with microfluidics was, if you're trying to measure small things, if you're looking for tiny things like proteins or virus or hormones that are really small, then you use small fluids and small containers. And that sometimes gives you some advantages, right? It might let you really detect very, very small amounts of things. It might mean that you don't need a ton of blood to see if you have a particular disease or condition. It might mean that you can have a really portable device.  

  So, there's a lot of benefits to using microfluidics. And people have been working on this for a really long time. And there are some like success stories that are – it's not all a scam. There are some success stories of things that have actually been deployed and developed and translated into the real world, sometimes in like a background-singer kind of mode, sometimes that's the main author. It has been around for decades now. Specifically for me, one of the things I really ask myself is, as an academic, as an assistant professor that runs a lab of eight graduate students, what are the areas that my lab? What are the unmet needs? What are the places where we can bring in creativity and new ideas and address the diagnostic needs that we can do today? Because, you know, for example, I'm never, I'm likely not going to out-compete a company of 1000s of people to do something. But where are the unaddressed needs? And where are the new frontiers in diagnostics that we need to go into? So that's kind of how I think about it.  

  Liz: It's like you're talking about what motivates you to do these things, and how you find problems that you think need to be solved in a way that are synergistic, both to the skill set you have, but also like the need that is there  

  Ayo: Exactly.  

  Liz: And so, I think this is, like a really interesting intersection, and I think that's something, you know, that we should all strive for. You know, I think you actually answered the career question first, because the way that you sustain yourself in this career is that you have to have alignment of what you're doing, the greater purpose. That's important to you, because the days are long and the money is not, I don't know. So we're I want to get back to that career question, but maybe talk about something you've touched on a little bit, which is like, what is microfluidics? Is this just like small fluid flow? 

  Ayo: Yeah, small volumes of fluids, like microliter quantities of fluid. So this is, you know, a drop of blood is, depending on how deep you cut and how much you squeeze, is somewhere between 10 microliters, maybe 50 microliters, you know, like, it's a very, very small drop, right? And so, this is the idea of, can we manipulate these small quantities of liquid and look for things in them. 

  Liz: And I think the other thing is that microfluidics, they have different properties than, I don't know big fluidics have.  

  Ayo: Yes, exactly.  

  Liz: The way they transport. And so literally, this is like a different physics. So, what are the ways in which working with smaller volumes give you different properties, and working with like, I know, a drinking glass of water, or water flowing in a river, might give you?  

  Ayo: Exactly. So, really good question. If you're dealing with small volumes of liquid, you're not going to use your cup at home, right? Then you need a small container, right? So then you're dealing with micro channels now to deal with your micro scale liquid volumes, right? And so, you could be dealing with a micro channel that's about the width of your hair, so say, like about 100 micrometers. And as you were saying, there are now different physical properties that are at play. So, one really famous one that people we often talk about in microfluidics is that mixing can be very challenging. Things don't automatically mix. There's something called laminar flow. If you put two fluids together at this small size, size scale, by default, they'll just kind of flow side by side, and they won't automatically mix. And to mix it, you have to almost really try to mix. Meanwhile, if I'm pouring milk into coffee, they just naturally start to mix. And so, there's some really cool YouTube videos where you can actually watch a video of like, what mixing looks like and what unmixing looks like. So, it's almost like if you had like food dye and soap, and you had, like, blue dye and soap and red dye and soap, and you put them side by side, they won't automatically mix, because the soap is really viscous. And when you get down to, like, a microfluidic scale, mixing becomes challenging. So that's one of the things to think about. What else? Mass transport and like heat transport also is different. So, for example, if you have a very small volume of liquid, then it's very easy to heat it up. So you can get it really, really hot, very fast and really, really cool, very fast. So that could also mean, if you're trying to do something that requires you to heat something up and cool it down, and heat it up and cool it down, just by going to a really small volume of liquid, you can do that way, way faster, because it just less of it. 

  Liz: So that also seems like, when it comes to the kind of tests you're doing, or the things that you can do, is making it smaller, makes it more possible, more feasible. Because, I guess it's going to be cheaper, because you require less energy to go in to make some of these processes happen, and it's going to be faster. There seems to be some really big advantages from this. What are some of the challenges? Then,  

  Ayo: Where do we begin? There’s a cost to the benefits, right? There's this idea that maybe you could be smaller, faster, better, stronger, whatever.  

  Liz: Daft Punk. 

  Ayo: So there's all of that. And then there are challenges, right? Because then you have to really work at these size scales now. So, you need to understand the physics. They need to actually be able to make something at the right size scale. And you want accuracy, right? Because any variation you introduce in making your container is then a variation that sort of, like ripples down the line. So there's just, like a fabrication thing that's actually that's better now in that, back in the day when I was a graduate student, the way you would make- 

  Liz: Back in the day? 

  Ayo: You know, back in my day, the way you would make these small channels was that we would go into a clean room, or what we call a clean room, which is very similar to the kind of facilities that Intel uses, and you have to gown up because, as I said, if the channel is about the size of your hair, if you get a hair on your device...  

  Liz: It floats over. 

  Ayo: So, we would go in at the time and we would buy photo masks. It's kind of like an old school photography process called Photo lithography. It's just writing with light. So, a photo mask that was like glass and chrome, $2,000 I would spend $50 an hour in the clean room, and this is just to see if I can, like, make your prototype that works. So, you can imagine poor grad student me walking in being like, I just spent $2,050 - at least - of my advisors money, and I forgot to put an alignment mark somewhere, right? So, you could spend a lot of money and time, right? Also, some of the chemicals we used were things like hydrofluoric acid.  

  Liz: Oh, that's really dangerous. 

  Ayo: It’s bad. You know, like, just learning how to do it was a challenge. Now, it's very easy to 3D print some of these things with a $200-$300 printer. It can print it in 30 minutes without paying $50 an hour. So, it's a lot better on the fabrication end. But one challenge is like fabrication. And the other end is like actually understanding the physics of what you're trying to do, what the design principles are, what the scaling laws are, what does that mean? And then one other thing or place that starts to get challenging is when you start to move from very clean systems, like where you're just looking at water or food dyes moving around to like biological materials and biological substances like blood, like saliva, like urine, biological materials are very variable, lots of person-to-person differences.  

  Liz: Oh, that's true, yeah. 

  Ayo: So it can get complicated very, very quickly. 

  Liz: So, but you told me that this was going to be easy- 

  Ayo: Did I? 

  Liz: I'm being lied to already. Wait. So then, okay, so you can use the properties of materials to move, separate, and do things that can kind of isolate. You can make a test, because if you know what you're testing for, you can make sure the thing you move moves at a certain rate, and kind of goes and does something interesting. And let me also just say you guys should look up some of his papers that Ayo has done, because he's been successful in making some tests already. So, I've seen some publications that are coming out of your lab or bio archive as well. So, in progress about antiviral drugs, for example. And then some of them use some pretty sophisticated techniques. And so how do you think about how you've designed those antiviral tests?  

  Ayo: Yeah. 

  Liz: Don't be humble. 

  Ayo: I'm trying to think of where to begin. So, I told you I do a lot of microfluidics. But what's interesting is there's a big part of my lab right now. I would say 60, 70% of my lab focuses on measuring antiviral drugs. And we started working on that because we had clinical partners that came to us and said, we know that in clinical trials and routine care, treatment often fails because people don't have enough medication in their system, so can you make a test that we can use within a doctor's office visit to see if somebody has enough drug to be protected. And I came in as a microfluidics person being like, I'm going to microfluidics this thing. 

  Liz: Like, I know heat transfer. I know mass transfer. I got this. I got some heat printing. 

  Ayo: I came in very much with, like, a hammer, and just  everything's a nail. I'm going to use my microfluidic skills. And we did not do much microfluidics for years, actually. 

  Liz: Interesting.  

  Ayo: It turns out that what we needed to do was to go back to the drawing board and say, what are these drugs that we're trying to measure? How do they function? What does measuring these drugs look like? Are we trying to measure the version of the drug that somebody took? Are we trying to measure the active form of the drug in the person, a drug buildup? How do people normally measure this? Are they using an antibody based test, something like a covid test, or are they doing mass spectrometry in the lab with a $200,000-300,000 instrument? In our case, for these antiviral drugs, it turns out that these drugs block the ability of certain enzymes or certain molecules in HIV to make DNA. Because we live in the DNA era, the ability to make DNA is a very easy thing to measure, and there were papers from the late 1990s that had looked at the ability of these drugs to block the ability to make DNA. So basically, we went back old school and we said, okay, measure these. And then we said, what are the techniques available to us today? We live in the CRISPR era. We live in the era of DNA dyes. I can buy DNA from Iowa, and it will- 

  Liz: I can buy DNA, I’ve got this. 

  Ayo: So, we've actually just been doing a lot of basic biochemistry, DNA synthesis, tools that many undergraduate students will use in, like their classes, in their labs, and we're taking that and applying it and measuring it and tailoring it for HIV drugs. And then where some of the test making and device making and microfluidics comes in is- So, we can do this thing in a tube in a lab, somebody's doing these things. How do you make these things self-contained? How do you make them something that runs by themselves, and not a person being involved. So that's where the microfluidic steps come in. But we first built the foundation of, what is the test itself, what's the simplest form? What are we measuring? What does it look like? And then we build around it,  

  Liz: I see, and that's a really good lesson. You know, we come in with the hammers, us engineers, and we think this is the property that needs to be solved. And often in collaboration with the people on the ground, you realize that's not the real problem, or we have to solve some other problem first to really clearly identify what the question is.  

  Ayo: Exactly,  

  Liz: I.E., what do we need to be measuring that can be done? And then you go back and do the scaling to say, Now, how do I make this, something that's reproducible, that's testable, and that someone who is not an expert in microfluidics, they can take this test and now understand it? And then also, how do I make it cheap? Because this needs to be scaled. 

  Ayo: Exactly.  

  Liz: Like that's around- that's everything that- you're doing everything! 

  Ayo: Yeah, it's, it's hard, and, as you said, right? It's like the prioritization too, right? And also, you know, I don't want to give the impression that this is somehow a solo thing, like, there have been so many collaborators, colleagues, who have offered ideas here and there and like, so it's almost also like building on the wealth of knowledge of what people have tried and sort of keeping that North Star of, like, what is the problem we're trying to solve? Like, how do we get this into people's hands?  

  Liz: And I saw you also had done something in prostate cancer, doing like, a test here. Was that a test that was going to be something that the doctors would then employ? 

  Ayo: Very good question, the prostate cancerone is new, actually. This is something that my friend and colleague, Etienne Audet-Walsh, who's a professor at Laval University in Canada, he reached out to me because after we published our HIV work, Etienne, so the backstory is, Etienne and I were in grad school together. He was a postdoc when I was a grad. He actually ran my writing group like I was doing a thesis writing group. And now he's a professor at Laval University. We published our work who are measuring these HIV drugs. But these HIV drugs are really just very small molecules, and normally people measure them with mass spectrometer, which is sort of the gold standard measurement. 

  Liz: The gold aspect is like 200,000. It's a difficult- it's a very great instrument, but it's very complicated to operate. You need a specialist, and it is very expensive and requires upkeep, and it's just, yeah, not really translatable,  

  Ayo: Exactly. You know, you might have it if you have a very rich and very well resourced lab, but it's not something that you would have in your home or in a lower resource or resource constrained setting. So, Etienne studies how cancer affects the way that your body metabolizes things. He studies the small molecules that show up or go away when you have cancer. And he's worked with prostate cancer for many years, and one of the things that his lab has found is that the prostate cancer actually changes how the prostate deals with sugars and metabolites and so things like citric acid, glucose,  

  Liz: Right, right. 

  Ayo: Sort of like your classic Krebs cycle. Yeah. Many cancers, it goes out of whack. In prostate cancer, it's actually really unique, partly because the prostate has a really weird metabolism to begin with. A lot of its metabolism is skewed to keep sperm alive for fertility as a really unique metabolism. And then when you get prostate cancer, that metabolism goes completely off key. And so, I've been studying this prostate cancer metabolism with mass spec. And then he saw our work on HIV, and it was like, Oh, we can measure these small molecules that you normally use mass spec for. And then you reached out, and it's like, do you think you do that for prostate cancer? These are very different small molecules, you know- 

  Liz: A very academic way. Academics are so terrible for this. Because well "Like, no, I use molecule one. You want to use molecule number two. I don't know. Like, they both are molecules, but that's just dramatically different. I can't do this, right? We like, we get so in our little silo that we're like, we can't  - we know we can't do that. 

  Ayo: It's interesting. I think, I think some of this also is like, as you know, right? We write grants and we write them as aspirational, but I think especially when I want to work with people and collaborators, I don't want to be somebody who says more than he can do. So, I think some of it also is the sense of, I don't want you to- I want to reflect where we are, so that you don't think it will be a magic snap, like this will take time. But then he was persistent. We wrote a couple of grant proposals together, the first one was submitted to the government of Canada. We didn't actually have anybody, time, or space to work on it, but now we've got some funding from the Canadian government. His lab has some funding a little bit here in the US. I have a graduate student who's very enthusiastic about working on this. And the idea, as you said, is to have something that doctors could use, but potentially even nurses or minimally. And so we're very, very early stages here. And actually, one interesting link between this one and the HIV one is this knowledge of how the prostate metabolism shifts has been around for many years, and it's also, can we then make it into something people can actually use? So, I think that's kind of a common thread between these things. Like, we build on established literature, but then use the tools of today to make it more available. 

  Liz: Hashtag, fun, basic research. The number of times that this has really come up, and how hard it is to backtrack, like, what those successes are. Yeah, it's really important. It's really important to highlight this, and also to be able to go back. Because sometimes the technology, the knowledge that we make a long time ago, we can't use it yet because we don't have the right technology. Or there's another piece of information that, another field generates that then together, it makes the sense. 

  Ayo: Oh, 1,000 percent. 

  Liz: So, it's such a beautiful kind of crazy thing to be in. And for me, science has always been interesting because of the human, the personal aspect. It was like such great storytelling. Because if this were a movie, you know, you can imagine as a movie, this would be like, you know, person #1 does something. You know, they do that. They will flashback like, 35 years ago, and then you flash forward to today, and someone's doing this study. And, you know, the person watching, oh, my God, what are they going to meet? When's the meet-cute? Is that what they call them, meet-cute? And then when's the success story? And they're going to solve cancer, and then they're going to do that, like, the credits are going to roll. They were like, Ayo, Who is still doing research. He won the Nobel Prize, and he won this, and then the other person's doing this other stuff, and they're all happy, right? You know, I would watch that story, I would cry. I would eat popcorn. I would cry in the movie theaters. 

  Ayo: It's beautiful. There's so much serendipity, like with our HIV work, right? Like there's papers from the early 90s where, even when they were just figuring out what HIV was, one of the ones figuring it out was looking at how these drugs block HIV, DNA synthesis, like this is very core, what is this virus? How do we treat it? How do we rush out a treatment for all these people that are dying. And then there's like, an interesting chapter in, like, the early 2000s where there's, like, a group in France that, like, randomly finds that these drugs build up in red blood cells. The drugs are not supposed to go in your red blood cells. They found them because it was contamination, but they paid enough attention. But because it builds up in red blood cells, there are so many red blood cells, and red blood cells are super wimpy. It's super easy to measure, so it's just like all these little pieces. And then we're borrowing stuff from the group who do CRISPR for all their things. I'm like, together all these little- all these like big milestones, like across decades, and I feel, in some ways, almost like my role as a scientist. Yes, we like we work hard, we're creative, but I also feel like we're almost like guardians of technology that's been around over the years. 

  Liz: I want to go maybe think about some of your motivations for this work that you do. Why do you think diagnostic testing that is accessible and like for on-demand testing is so important? I mean, LabCorp seems to be doing the job pretty well. 

  Ayo: You're right. And I think you know our dominant model in the US and many other similar countries and similar spaces is this idea of like a very centralized test, and then you send them, you batch them back, and that model works. You can kind of reduce the cost per test. You can get these massive instruments that are run, and that works for a lot of things. My interest in diagnostics that are available in point of care or sort of point of need settings, is very related to my own origin story. So, I grew up in a small town in the southwest of Nigeria in a small town, and my town has two main employers, a university teaching hospital and a big cement factory. And a lot of people I knew were either doctors and nurses or engineers, and that was a big privilege, right to be working in this space where, like, healthcare had such a central part. Another big privilege I had was, like, both my parents were in healthcare, and at one point, as part of their side hustle, they started a clinic. And so I lived right next door, actually, at one point, our living room was part of the clinic. 

  Liz: Very on point, on-demand. 

  Ayo: And then eventually I lived right next door to a clinic, to a pediatric clinic, and it was interesting to see, right because then lab testing meant, okay, take the sample, send it to the lab, bring it back to see the role that just having all that information in hand at that point, like, what that meant for people has been to imagine other models of care that are not that everything is centralized. And then, you know, going through undergrad, grad school, thinking about point of care diagnostics and thinking about, like, you know, when you need things to be really cheap and right there and readily available, like our current model where you're saying, you know, people do really well with this, like a tens of thousands or hundreds of thousands of dollars machine does not fit in many of those contexts, right? And then if you throw in, like, the fact that a lot of the infectious diseases, and even a growing number of non-communicable diseases like cancer, affect people in low and middle income settings, right like, or even in our high income country, the US is not a uniformly high income country. There are many people who don’t have access to care. So if you throw in that part that, like, our current diagnostic system doesn't necessarily serve everyone, and if we could make tests cheaper, faster, better, more available, just to think of like how many people could potentially benefit from, like, personalized care, like what internet we had provided that people don't have. So, I think growing up close to a different healthcare system, seeing how much need there was, for me, has just always made it so that whatever work I do, I really, really do want it to touch people and impact people. That's where a lot of the motivation has come from for me. 

  Liz: I mean, that's huge being able to see that. And, you know, something that stands out to me, and I want to make this point, is that you started to start by talking about how you grew up in a small town in Nigeria, right? And I think that for a lot of listeners who are American, you know, they may think, oh, this doesn't apply to me. This is a global health problem. And I think, no, actually, there's a lot of rural settings that could benefit from this. Or let's consider, you know, I'm from a town that many people who don't live in cities, let's say if you get cancer, you may end up going to a comprehensive cancer center that your state may not have. You may travel two, three hours to get to that comprehensive health center. And then the people that you see locally, you know, may or may not have that expertise, but would sure darn like to have some sort of test to be able to monitor you in between getting these big treatments. And so, there's just so much about even being in this space of rural versus urban setting, or just even you could be in the city, but it's not the state that has the right care that you need, or if the appointments are too long. But if I had a test, you could go in and get a test and get the assay, and people can kind of like, really gauge you and do more time tracking. Science is like data points. Medicine is data points, and so they want to know what your baseline level was, but if you only got your test done once and it's two years later, that's not enough information for most diseases to tell you if something's changing. But a diagnostic test allows you to take it to your heart's content, almost. Right, like I can test every day and get a nice little curve of you know, how things are really changing. And I think it's really important that important to think about how, even in the US context, or in like a first world, you know, it's so important, and you can use this, and it can be really helpful for all types of healthcare settings, and maybe bringing the power back to the community, you know, bringing it back to your local doctor, even to you to have that power, to say, I can take part in this testing. 

  Ayo: Yeah, absolutely. And you know, we have a collaborator, Dr. Chris Blosser, who works at the Center for Innovation.  

  Liz: Oh, I love Chris. Oh, he's amazing. 

  Ayo: And you know, it's, it's been wonderful to be included in focus groups they have with patient advisors and hearing about people traveling from like, thousands of miles away to get care. And so, the need, even here in the US is great. 

  Liz: Especially as we shut down those clinics. The reduction in federal funding has meant that many local hospitals have been shut down, and now we're getting more to the centralized, yeah. So, it affects everyone. Sorry, it's one day.  

  Ayo: It's so important.  

  Liz: How big can this get? So, you mentioned we talked about two use cases where we talk about, let's say, antivirals, antiviral medications for HIV, context, citrate for prostate. Is it actually feasible to have a test for everything? Tell me what the kind of limitations or scope that you might think this would have. 

  Ayo: So I think one of the things that's really interesting is, right, there's a catalog of tests that's currently being done in clinical laboratories. 

  Liz: What do you mean catalog of tests? 

  Ayo: Like, you know, if you're sick, right now, there's a number of tests that people would do, right? There's like, sort of complete blood count. There are commercial companies, like, there's the ice stat platform from Abbott that can measure cardiac trope- like, there's a number of things that are available on the market right now that people can do. Some of them people choose to do, whether for logistical reasons, whether for financial reasons. Some of them people use choose to do in a centralized format. For some of them, because there's not the same urgency or need for point of care, but some people choose to do them in a centralized way. But then there are many others, whether it's because you need it in an emergency room or it tells you other things that are more point of care. And I think one of the things that's also interesting, I kind of led off with a Theranos example, is like, there is something really compelling about having more accessible diagnostics, giving that information to people. So, from a technical point of view, a lot of tests are feasible. A lot of tests could be done as point of care tests from a societal, economic, like the what do you need? The results, same, you know, within a 15 minute period, for a one day period? What is the difference in cost? What is the difference in quality? What are the tradeoffs? Like, those questions I think, are really important. What do you do with this information when you get it, like, what does it mean? So, I guess I'm trying to walk this line of, like, a lot of tests are possible, which tests are necessary? How do we prioritize them? And in what scenarios does a point of care test give you more actionable information than a centralized test? 

  Liz: I love what you just said, because it also highlights the interaction between the science and then the implementation, which is like a public health question. So, what do we as a society need? The finance question, what can we afford? You know, what the real benefits of human health are? And then oftentimes, when it comes to, like, whether something actually goes into the market or it actually helps people, whether they actually get to see that is not about our capacity as scientists and engineers to dream up or ideate to make something happen. It really does take all of these voices and kind of converging on something that really lands like that's when something goes viral, so to speak. 

  Ayo: Exactly, exactly. 

  Liz: And that's how it should work. Sorry, I think about this one. I remember this one time in class. Yes, I had students, and they were asking, we're talking about covid, I think I was talking about just the disease, and someone just said, like, we're asking about masking, something like this. And like, Do you really think that could have stopped the pandemic or something? And I remember kind of, you know, I pointed out to them that the question that you asked me is not as simple as it sounds like, because if you ask a scientist, they're going to say, mask and vaccinate, and then it won't happen, right? And then, and public health says, yes, here are some strategies that maybe people can tolerate, that we could do, but you could also ask other people, and they would say, well, what's the cost of isolation when we know that people who aren't going to get cancer treatment or are getting delays in diagnosis, they're going to die in these other ways. Or how do we deal with, like, the other implications of this? And then what are the political implications? And so, it's talking about all this intersection, and how these questions aren't as easy. And so, you asked me a question, and as a scientist, I would probably undoubtedly say this one thing, but you need to understand, like this role that you have in this intersection that is not actually just a pure science question. Every question has heart, has something behind it, which I hope that people- when I hope my students listen to when they hear things like that, is to hear how powerful it is and how exciting it is to be a scientist, or even just to engage in the science, because science is about- for as much as it's about, you know, the purity, you know, like, the answer is two. It's just the answer is two. That's just what two plus two is for. That is what we know. But also, just because two plus two is four, like, you still have to think about what that means in the context of whatever else you're studying. And so, that interaction, and that's where they get to pull in their own experiences and knowledge in your communities and like where they grow up, where they live. And you know, it gets me really exciting. 

  Ayo: I'll give you an example with our antiviral drugs, the first ever HIV conference I went to. And what's really cool about HIV conferences is you actually have patient advocates show up. And I wasn't used to that, because I go to all these talks and it's all scientists. And here, patient advocates from multiple continents show up, and I think some of this is related to the history of activism that was required to get HIV medications developed, even in the first place. This conference, and we're talking about HIV medication monitoring, and somebody comes up and says, hey, what does this mean, if you're measuring HIV medications, how do you make sure this isn't used in a punitive way to say you're not taking your medications? Well, I'm not going to pay for it. Like, how do you position it instead, in a way of, how can I use this information, provide this information to support people, to get better? How do you loop that in with insurance? What does all of that mean? How do you not use it in a way to effectively... 

  Liz: Yeah, what did you say?  

  Ayo: Oh, thankfully, they weren't asking me. It was very humbling, though, because I went back to my lab and I was like, oh, everybody that's asked me about this is a clinician who's very much to fact check. And how do I make sure that what we build is also useful for the person going in, who maybe has a different point of view, who maybe knows whether or not they're taking their meds, but they want to know something else, like, how do I make sure that I don't only reflect one perspective? That was really humbling. Yeah, 

  Liz: Also, I think it's useful to have people, even who aren't going to become professors, still do the classes and do the things that we're talking about. So we can make tests for everything. But should we and then how? But that's the question that's both difficult and also the one that's fun. 

  Ayo: A Caveat there for everything is that there's still some technical limitations, right? So how you spot a scam is, if I'm trying to make a test for HIV, my test, you know, to be clinically useful, needs to be able to detect- I'm just going to throw a number out there that the World Health Organization recommends- say, 1000 copies of HIV in a milliliter of blood. Then I need to think about the math. 

  Liz: Like a teaspoon. 

  Ayo: Can my test actually physically do that? Like, are there technical limitations for what my test needs to do? Will I have enough sample in my tiny little bit of blood for the point of care version that will actually work or do I need a full blood draw. So, there's still some technical limitations, right? Some things are just really rare, right? So you need a little bit more blood than that. Some things you can't measure in a really small volume. Maybe some you can't, or maybe you can measure them in a really small volume, if your test was wonderful, right? It's one of those things where, like, you have to, yeah, there are still technical limitations. But for a lot of things, we can make them; should we? How much will it cost? But I wouldn't say we can make tests for everything because it depends. Everything is big. You're getting the, like, scientist of me being like, I'm sure there's some exceptions. 

  Liz: Then you're also doing, you know, you're trying to save the field from the Theranos curse, where people may not believe. They did sell that we can do everything in this one little vial, and that's not scientifically accurate, as much as we want to sell dreams. Okay, you know what? I do still feel more comfortable with what you're saying, and I believe in this future that's really cool. 

  Liz: So we're about to head out Ayo, but before we do, I want you to answer for our listeners, what would a world without biomedical engineering look like? 

  Ayo: You know, biomedical engineering is so broad and so infused in so many different parts of our lives. You know, like we talked about diagnostics and tests today, but then there's prosthetics, there's imaging, there's immunoengineering. There are so many different aspects of our lives, where biomedical engineering is either a big or small main character, supporting character. It's like, infused into so many different aspects of our lives. But I really struggle to imagine a world without biomedical engineering. It would not be a very healthy place, like between figuring out if we're sick, treating those diseases, there would just be so much that would just be gone instantly. I'm trying not to be hyperbolic, but like, it would be, we might as well just like, do bloodletting and eels and like, might as well go back to that. Maybe there was some engineering there. It just wasn't very sound yet, 

  Liz: I think the world would be knowing that citrate does things, but not knowing how to help us understand whether someone has prostate cancer or not. It would be knowing information and not knowing what to do with it to help people. 

  Ayo: It actually has me thinking. I talk about this in my class, sometimes in my microfluidics class of how pregnancy tests as we think of them today, if you go 30-40, years, maybe to the 70s, there wasn't a pregnancy test that was a portable thing you could use in your home. You think about wearable glucometers for diabetes, right? Those things didn't exist. They were just not thought to be possible. And now it's just like an indispensable part of our day-to-day life. And so I think of biomedical engineering as something that bridges, that creates possibility and can transform our lives in a way that when it becomes reality. Was there even a world where this never existed? 

  Liz: What's most exciting for you these days? 

  Ayo: What's most exciting to me now, I think you know this HIV work that my lab does. So, I started this when I moved to the University of Washington in 2018 as a postdoc. Then I started my lab in 2022 and we've been working on this for over seven years now, and it feels like we're finally getting to the point where what we have is useful, like where what we have, people are asking us, how many can I get? How many can you do? And it's really exciting to feel like we're on the precipice of moving something outside, and that's- it feels like we're shepherding something into the real and it might be useful. I mean, even today, like one of my students was testing out a new drug that we'd never tested before, a new antiviral trial. But because the science is sound like it looks like we can measure it, so it's really exciting to feel like we have something useful,  

  Liz: Like something's finally working. And you, I know you've gotten, like, a few recent, I guess, pilot grants, or like some for commercialization properties. So that's exciting. 

  Ayo: And it's a lot of learning too, because I've never actually commercialized something before, right? I've been part of different aspects of that never actually been out  of the lab. So it's really exciting. And I think, separate from the almost problem and mission driven and people aspects, there's also just, like, a lot of curiosity of what else is there? Like, what are the boundaries of our knowledge? What other ideas, what other things can we do? And some of these things come from collaborators, right? So, for example, our colleague here at University of Washington Professor Alshakim Nelson, who's now the chair of chemistry, when I started my lab here, reached out, and he was like, you know, we 3d print materials to have microbes in them. Like, he's just, building materials. Do you think you could make micro channels to keep them happy? And I was like, You're asking me to build plumbing for back here, it was like, Yeah, asking me to build plumbing. And so every week, I meet with one of my grad students who's working on this project, or we're 3D-printing materials that have yeast in them, in little micro channels to feed them and keep them happy. And it's, it's like directions I never thought I would go, these things I never even thought were possible. And we don't really know where any of that will go, but it's just, you know, there are these two sides now, where there's like, Let's get something out into people's hands. And then the academic in me also then has this great privilege to be able to work on things that are like, we don't even know where this will go, but it's really cool. The videos lovely, and we're like pushing to the limits of our knowledge constantly. And that's also really, really exciting a very different and complementary way. 

  Liz: Yeah, that's exciting. I mean, when I first heard about using micropholytics and microbes to build buildings, right? They can make structures and be a part of, we can think of, I don't know, the next building that we work in, instead of being made of wood, it's made of microfluidics that support fluid and nutrients going into little microbes, you know, that kind of excrete these material properties. 

  Ayo: Yeah, I mean, speaking of funeral fending like, we got some money from the NSF for this. And it involves people who do synthetic biology, who engineer bacteria or yeast, cool things. It involves chemists who make the materials, involves us engineers. We also have an architect who runs the studio class every year where she asked students how do you imagine these buildings would look like? What would you like to know? Do you want to use them for sensing? Do you want to use them for shade? Do you want to think about them from like a weather point of view? Do you want to make materials that are self-healing if they break the microbes. And, you know, so it's just really cool to imagine a future, and it's really cool as an academic, to not only be able to work on things that might be useful today, but then to do things that we can imagine for a tomorrow that we don't even know what that looks like. And I think that's part of what makes this job also just so enjoyable. 

  Liz: Just think, Ayo, someone some years from now, will go: I found this really old paper by Ayo Olanrewaju’s lab, and they did this thing. And they were like, we don't know what to do with it, but here you go. And now we're like, oh, wow, wait. Actually, I can do that now. Hey, Professor, I found this old paper and it's going to be useful, and then all of a sudden, boom. 

  Ayo: I can't wait to be that old. It's like, yes, the baton has been passed.  

  Liz: It's the goal, right? The baton has been passed. And also, thank you grant funding, you know, for supporting all this work, right? That's so exciting. Do you have any cool conferences or papers that you're excited about? 

  Ayo: Yes, yes. So actually, before we got on this call, one of my graduate students, we have a couple of papers right now that we're about to submit. One of them is taking our antiviral drug measurement platform and taking them and adapting them for drugs that people use to prevent viral infections among transplant recipients. We're kind of pivoting a little bit and looking at a different use case, and then the other paper, and both are really close to us submitting them now, the other one is focused on using the same platform, and instead of looking at how much drug people have in their systems, using it to look at whether or not somebody has now developed drug resistance. And so that's another paper that I'm really excited about, just to kind of see that work reach the end of the first chapter, if you will. We still have a lot to do, but kind of see those reach the end of the first chapter, and then the next conference I'm excited about. There's a conference called the Conference on Retroviruses and Opportunistic Infections, or CROI for short. CROI is a fun conference. It's one that has a lot of patient advocates. It has a lot of like, big clinical trials. It's just huge. It's huge. And it's in February every year, and I'm really looking forward to going. We just found out that we got an abstract accepted to present there, so yeah. 

  Liz: Oh, that's awesome. Oh my God, there's so much to follow up on. And so, for all the listeners, we're going to make sure that you can see some of the work that Ayo is talking about. We'll put links in the comment section or in the bio. This has been so much fun. It's been so great. You're so humble and so nice, so you're too nice to know you're doing really well and making some really good progress. And then I was going to say, one day I was going to use one of your tests, but it's just really exciting, and it's nice to see you. So thank you so much for coming on the show. I have learned more about diagnostics and how and when to use them and all these different applications, and about the journey. And really, I love how you've connected your mission and your science, and like what you view as being a professor and your contribution to biomedical engineering in society. So, thank you. 

  Ayo: Thank you for having me. Thank you for the thoughtful questions. Thank you for the scenarios. It was fun to read real world situations and it's yeah, thank you for doing this. I think now especially, it's so nice to think about how the work that we do is related to people's day-to-day lives, and what it is to get to do this. So, thank you. Thank you for having me on 

  Liz: if you have questions or ideas for a future episode, feel free to email us at communications@bmes.org. You can also stay up to date by following us on social media at bmesSociety and visiting our website at bmes.org/podcast/officehours. We look forward to hearing from you, and maybe we'll feature one of you on the podcast. 

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